Overview

Dataset statistics

Number of variables19
Number of observations30000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory152.0 B

Variable types

Numeric16
Categorical3

Alerts

PAY_0 is highly overall correlated with PAY_2 and 2 other fieldsHigh correlation
PAY_2 is highly overall correlated with PAY_0 and 8 other fieldsHigh correlation
PAY_3 is highly overall correlated with PAY_0 and 9 other fieldsHigh correlation
PAY_4 is highly overall correlated with PAY_0 and 10 other fieldsHigh correlation
PAY_5 is highly overall correlated with PAY_2 and 8 other fieldsHigh correlation
PAY_6 is highly overall correlated with PAY_2 and 8 other fieldsHigh correlation
overdue pay_mon1 is highly overall correlated with PAY_2 and 7 other fieldsHigh correlation
overdue pay_mon2 is highly overall correlated with PAY_2 and 9 other fieldsHigh correlation
overdue pay_mon3 is highly overall correlated with PAY_2 and 9 other fieldsHigh correlation
overdue pay_mon4 is highly overall correlated with PAY_2 and 9 other fieldsHigh correlation
overdue pay_mon5 is highly overall correlated with PAY_3 and 8 other fieldsHigh correlation
overdue pay_mon6 is highly overall correlated with PAY_4 and 7 other fieldsHigh correlation
ID is uniformly distributedUniform
ID has unique valuesUnique
PAY_0 has 14737 (49.1%) zerosZeros
PAY_2 has 15730 (52.4%) zerosZeros
PAY_3 has 15764 (52.5%) zerosZeros
PAY_4 has 16455 (54.9%) zerosZeros
PAY_5 has 16947 (56.5%) zerosZeros
PAY_6 has 16286 (54.3%) zerosZeros
overdue pay_mon1 has 2139 (7.1%) zerosZeros
overdue pay_mon2 has 2503 (8.3%) zerosZeros
overdue pay_mon3 has 2890 (9.6%) zerosZeros
overdue pay_mon4 has 3118 (10.4%) zerosZeros
overdue pay_mon5 has 3364 (11.2%) zerosZeros
overdue pay_mon6 has 3854 (12.8%) zerosZeros

Reproduction

Analysis started2024-06-30 11:43:13.939012
Analysis finished2024-06-30 11:44:34.746840
Duration1 minute and 20.81 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct30000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15000.5
Minimum1
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2024-06-30T11:44:34.930777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1500.95
Q17500.75
median15000.5
Q322500.25
95-th percentile28500.05
Maximum30000
Range29999
Interquartile range (IQR)14999.5

Descriptive statistics

Standard deviation8660.3984
Coefficient of variation (CV)0.57734065
Kurtosis-1.2
Mean15000.5
Median Absolute Deviation (MAD)7500
Skewness0
Sum4.50015 × 108
Variance75002500
MonotonicityStrictly increasing
2024-06-30T11:44:35.326983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
19997 1
 
< 0.1%
20009 1
 
< 0.1%
20008 1
 
< 0.1%
20007 1
 
< 0.1%
20006 1
 
< 0.1%
20005 1
 
< 0.1%
20004 1
 
< 0.1%
20003 1
 
< 0.1%
20002 1
 
< 0.1%
Other values (29990) 29990
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
30000 1
< 0.1%
29999 1
< 0.1%
29998 1
< 0.1%
29997 1
< 0.1%
29996 1
< 0.1%
29995 1
< 0.1%
29994 1
< 0.1%
29993 1
< 0.1%
29992 1
< 0.1%
29991 1
< 0.1%

LIMIT_BAL
Real number (ℝ)

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.32
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2024-06-30T11:44:35.658176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.66
Coefficient of variation (CV)0.77468541
Kurtosis0.5362629
Mean167484.32
Median Absolute Deviation (MAD)90000
Skewness0.99286696
Sum5.0245297 × 109
Variance1.6834456 × 1010
MonotonicityNot monotonic
2024-06-30T11:44:35.988833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 3365
 
11.2%
20000 1976
 
6.6%
30000 1610
 
5.4%
80000 1567
 
5.2%
200000 1528
 
5.1%
150000 1110
 
3.7%
100000 1048
 
3.5%
180000 995
 
3.3%
360000 881
 
2.9%
60000 825
 
2.8%
Other values (71) 15095
50.3%
ValueCountFrequency (%)
10000 493
 
1.6%
16000 2
 
< 0.1%
20000 1976
6.6%
30000 1610
5.4%
40000 230
 
0.8%
50000 3365
11.2%
60000 825
 
2.8%
70000 731
 
2.4%
80000 1567
5.2%
90000 651
 
2.2%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
800000 2
 
< 0.1%
780000 2
 
< 0.1%
760000 1
 
< 0.1%
750000 4
< 0.1%
740000 2
 
< 0.1%
730000 2
 
< 0.1%
720000 3
 
< 0.1%
710000 6
< 0.1%
700000 8
< 0.1%

SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
2
18112 
1
11888 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Length

2024-06-30T11:44:36.311881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T11:44:36.573023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Most occurring characters

ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 18112
60.4%
1 11888
39.6%

EDUCATION
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8531333
Minimum0
Maximum6
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2024-06-30T11:44:36.758474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79034866
Coefficient of variation (CV)0.42649314
Kurtosis2.0786216
Mean1.8531333
Median Absolute Deviation (MAD)1
Skewness0.97097205
Sum55594
Variance0.624651
MonotonicityNot monotonic
2024-06-30T11:44:37.091893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 14030
46.8%
1 10585
35.3%
3 4917
 
16.4%
5 280
 
0.9%
4 123
 
0.4%
6 51
 
0.2%
0 14
 
< 0.1%
ValueCountFrequency (%)
0 14
 
< 0.1%
1 10585
35.3%
2 14030
46.8%
3 4917
 
16.4%
4 123
 
0.4%
5 280
 
0.9%
6 51
 
0.2%
ValueCountFrequency (%)
6 51
 
0.2%
5 280
 
0.9%
4 123
 
0.4%
3 4917
 
16.4%
2 14030
46.8%
1 10585
35.3%
0 14
 
< 0.1%

MARRIAGE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
2
15964 
1
13659 
3
 
323
0
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Length

2024-06-30T11:44:37.447567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T11:44:37.894447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring characters

ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 15964
53.2%
1 13659
45.5%
3 323
 
1.1%
0 54
 
0.2%

AGE
Real number (ℝ)

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.4855
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2024-06-30T11:44:38.256727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.2179041
Coefficient of variation (CV)0.25976537
Kurtosis0.044303378
Mean35.4855
Median Absolute Deviation (MAD)6
Skewness0.73224587
Sum1064565
Variance84.969755
MonotonicityNot monotonic
2024-06-30T11:44:38.791094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 1605
 
5.3%
27 1477
 
4.9%
28 1409
 
4.7%
30 1395
 
4.7%
26 1256
 
4.2%
31 1217
 
4.1%
25 1186
 
4.0%
34 1162
 
3.9%
32 1158
 
3.9%
33 1146
 
3.8%
Other values (46) 16989
56.6%
ValueCountFrequency (%)
21 67
 
0.2%
22 560
 
1.9%
23 931
3.1%
24 1127
3.8%
25 1186
4.0%
26 1256
4.2%
27 1477
4.9%
28 1409
4.7%
29 1605
5.3%
30 1395
4.7%
ValueCountFrequency (%)
79 1
 
< 0.1%
75 3
 
< 0.1%
74 1
 
< 0.1%
73 4
 
< 0.1%
72 3
 
< 0.1%
71 3
 
< 0.1%
70 10
< 0.1%
69 15
0.1%
68 5
 
< 0.1%
67 16
0.1%

PAY_0
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0167
Minimum-2
Maximum8
Zeros14737
Zeros (%)49.1%
Negative8445
Negative (%)28.1%
Memory size234.5 KiB
2024-06-30T11:44:39.241684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1238015
Coefficient of variation (CV)-67.293505
Kurtosis2.720715
Mean-0.0167
Median Absolute Deviation (MAD)1
Skewness0.73197493
Sum-501
Variance1.2629299
MonotonicityNot monotonic
2024-06-30T11:44:39.684514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 14737
49.1%
-1 5686
 
19.0%
1 3688
 
12.3%
-2 2759
 
9.2%
2 2667
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
8 19
 
0.1%
6 11
 
< 0.1%
ValueCountFrequency (%)
-2 2759
 
9.2%
-1 5686
 
19.0%
0 14737
49.1%
1 3688
 
12.3%
2 2667
 
8.9%
3 322
 
1.1%
4 76
 
0.3%
5 26
 
0.1%
6 11
 
< 0.1%
7 9
 
< 0.1%
ValueCountFrequency (%)
8 19
 
0.1%
7 9
 
< 0.1%
6 11
 
< 0.1%
5 26
 
0.1%
4 76
 
0.3%
3 322
 
1.1%
2 2667
 
8.9%
1 3688
 
12.3%
0 14737
49.1%
-1 5686
 
19.0%

PAY_2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.13376667
Minimum-2
Maximum8
Zeros15730
Zeros (%)52.4%
Negative9832
Negative (%)32.8%
Memory size234.5 KiB
2024-06-30T11:44:40.097507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.197186
Coefficient of variation (CV)-8.9498079
Kurtosis1.5704177
Mean-0.13376667
Median Absolute Deviation (MAD)0
Skewness0.79056502
Sum-4013
Variance1.4332543
MonotonicityNot monotonic
2024-06-30T11:44:40.403582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15730
52.4%
-1 6050
 
20.2%
2 3927
 
13.1%
-2 3782
 
12.6%
3 326
 
1.1%
4 99
 
0.3%
1 28
 
0.1%
5 25
 
0.1%
7 20
 
0.1%
6 12
 
< 0.1%
ValueCountFrequency (%)
-2 3782
 
12.6%
-1 6050
 
20.2%
0 15730
52.4%
1 28
 
0.1%
2 3927
 
13.1%
3 326
 
1.1%
4 99
 
0.3%
5 25
 
0.1%
6 12
 
< 0.1%
7 20
 
0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 20
 
0.1%
6 12
 
< 0.1%
5 25
 
0.1%
4 99
 
0.3%
3 326
 
1.1%
2 3927
 
13.1%
1 28
 
0.1%
0 15730
52.4%
-1 6050
 
20.2%

PAY_3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1662
Minimum-2
Maximum8
Zeros15764
Zeros (%)52.5%
Negative10023
Negative (%)33.4%
Memory size234.5 KiB
2024-06-30T11:44:40.621782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1968676
Coefficient of variation (CV)-7.2013692
Kurtosis2.0844359
Mean-0.1662
Median Absolute Deviation (MAD)0
Skewness0.84068183
Sum-4986
Variance1.432492
MonotonicityNot monotonic
2024-06-30T11:44:40.856937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15764
52.5%
-1 5938
 
19.8%
-2 4085
 
13.6%
2 3819
 
12.7%
3 240
 
0.8%
4 76
 
0.3%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
1 4
 
< 0.1%
ValueCountFrequency (%)
-2 4085
 
13.6%
-1 5938
 
19.8%
0 15764
52.5%
1 4
 
< 0.1%
2 3819
 
12.7%
3 240
 
0.8%
4 76
 
0.3%
5 21
 
0.1%
6 23
 
0.1%
7 27
 
0.1%
ValueCountFrequency (%)
8 3
 
< 0.1%
7 27
 
0.1%
6 23
 
0.1%
5 21
 
0.1%
4 76
 
0.3%
3 240
 
0.8%
2 3819
 
12.7%
1 4
 
< 0.1%
0 15764
52.5%
-1 5938
 
19.8%

PAY_4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.22066667
Minimum-2
Maximum8
Zeros16455
Zeros (%)54.9%
Negative10035
Negative (%)33.5%
Memory size234.5 KiB
2024-06-30T11:44:41.090808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1691386
Coefficient of variation (CV)-5.2982113
Kurtosis3.4969835
Mean-0.22066667
Median Absolute Deviation (MAD)0
Skewness0.99962941
Sum-6620
Variance1.3668851
MonotonicityNot monotonic
2024-06-30T11:44:41.332352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 16455
54.9%
-1 5687
 
19.0%
-2 4348
 
14.5%
2 3159
 
10.5%
3 180
 
0.6%
4 69
 
0.2%
7 58
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
1 2
 
< 0.1%
ValueCountFrequency (%)
-2 4348
 
14.5%
-1 5687
 
19.0%
0 16455
54.9%
1 2
 
< 0.1%
2 3159
 
10.5%
3 180
 
0.6%
4 69
 
0.2%
5 35
 
0.1%
6 5
 
< 0.1%
7 58
 
0.2%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 58
 
0.2%
6 5
 
< 0.1%
5 35
 
0.1%
4 69
 
0.2%
3 180
 
0.6%
2 3159
 
10.5%
1 2
 
< 0.1%
0 16455
54.9%
-1 5687
 
19.0%

PAY_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2662
Minimum-2
Maximum8
Zeros16947
Zeros (%)56.5%
Negative10085
Negative (%)33.6%
Memory size234.5 KiB
2024-06-30T11:44:41.548915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1331874
Coefficient of variation (CV)-4.2569024
Kurtosis3.9897481
Mean-0.2662
Median Absolute Deviation (MAD)0
Skewness1.008197
Sum-7986
Variance1.2841137
MonotonicityNot monotonic
2024-06-30T11:44:41.786408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16947
56.5%
-1 5539
 
18.5%
-2 4546
 
15.2%
2 2626
 
8.8%
3 178
 
0.6%
4 84
 
0.3%
7 58
 
0.2%
5 17
 
0.1%
6 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
-2 4546
 
15.2%
-1 5539
 
18.5%
0 16947
56.5%
2 2626
 
8.8%
3 178
 
0.6%
4 84
 
0.3%
5 17
 
0.1%
6 4
 
< 0.1%
7 58
 
0.2%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 58
 
0.2%
6 4
 
< 0.1%
5 17
 
0.1%
4 84
 
0.3%
3 178
 
0.6%
2 2626
 
8.8%
0 16947
56.5%
-1 5539
 
18.5%
-2 4546
 
15.2%

PAY_6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2911
Minimum-2
Maximum8
Zeros16286
Zeros (%)54.3%
Negative10635
Negative (%)35.4%
Memory size234.5 KiB
2024-06-30T11:44:42.017409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1499876
Coefficient of variation (CV)-3.95049
Kurtosis3.4265341
Mean-0.2911
Median Absolute Deviation (MAD)0
Skewness0.94802939
Sum-8733
Variance1.3224715
MonotonicityNot monotonic
2024-06-30T11:44:42.238854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 16286
54.3%
-1 5740
 
19.1%
-2 4895
 
16.3%
2 2766
 
9.2%
3 184
 
0.6%
4 49
 
0.2%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
-2 4895
 
16.3%
-1 5740
 
19.1%
0 16286
54.3%
2 2766
 
9.2%
3 184
 
0.6%
4 49
 
0.2%
5 13
 
< 0.1%
6 19
 
0.1%
7 46
 
0.2%
8 2
 
< 0.1%
ValueCountFrequency (%)
8 2
 
< 0.1%
7 46
 
0.2%
6 19
 
0.1%
5 13
 
< 0.1%
4 49
 
0.2%
3 184
 
0.6%
2 2766
 
9.2%
0 16286
54.3%
-1 5740
 
19.1%
-2 4895
 
16.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
0
23364 
1
6636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Length

2024-06-30T11:44:42.484952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-30T11:44:42.743164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 23364
77.9%
1 6636
 
22.1%

overdue pay_mon1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23767
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45559.75
Minimum-733744
Maximum913727
Zeros2139
Zeros (%)7.1%
Negative4330
Negative (%)14.4%
Memory size234.5 KiB
2024-06-30T11:44:42.974498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-733744
5-th percentile-5095.95
Q1745
median18550.5
Q362241.5
95-th percentile191911.6
Maximum913727
Range1647471
Interquartile range (IQR)61496.5

Descriptive statistics

Standard deviation73173.789
Coefficient of variation (CV)1.606106
Kurtosis9.7909586
Mean45559.75
Median Absolute Deviation (MAD)18914.5
Skewness2.3003331
Sum1.3667925 × 109
Variance5.3544035 × 109
MonotonicityNot monotonic
2024-06-30T11:44:43.252342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2139
 
7.1%
780 62
 
0.2%
2500 61
 
0.2%
2400 38
 
0.1%
390 33
 
0.1%
-390 30
 
0.1%
-200 30
 
0.1%
1050 21
 
0.1%
-780 21
 
0.1%
-1 20
 
0.1%
Other values (23757) 27545
91.8%
ValueCountFrequency (%)
-733744 1
< 0.1%
-670580 1
< 0.1%
-422237 1
< 0.1%
-398516 1
< 0.1%
-380039 1
< 0.1%
-298887 1
< 0.1%
-296278 1
< 0.1%
-273955 1
< 0.1%
-262123 1
< 0.1%
-260148 1
< 0.1%
ValueCountFrequency (%)
913727 1
< 0.1%
726314 1
< 0.1%
624562 1
< 0.1%
605989 1
< 0.1%
602458 1
< 0.1%
599725 1
< 0.1%
590523 1
< 0.1%
586654 1
< 0.1%
585018 1
< 0.1%
581775 1
< 0.1%

overdue pay_mon2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23422
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43257.912
Minimum-1702347
Maximum933208
Zeros2503
Zeros (%)8.3%
Negative4529
Negative (%)15.1%
Memory size234.5 KiB
2024-06-30T11:44:43.559191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1702347
5-th percentile-5694.3
Q1329.5
median18102.5
Q359077.75
95-th percentile185663.85
Maximum933208
Range2635555
Interquartile range (IQR)58748.25

Descriptive statistics

Standard deviation72565.937
Coefficient of variation (CV)1.6775183
Kurtosis23.537988
Mean43257.912
Median Absolute Deviation (MAD)18432.5
Skewness1.4478358
Sum1.2977374 × 109
Variance5.2658152 × 109
MonotonicityNot monotonic
2024-06-30T11:44:43.869126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2503
 
8.3%
780 64
 
0.2%
-390 55
 
0.2%
2500 50
 
0.2%
2400 40
 
0.1%
390 31
 
0.1%
-780 31
 
0.1%
150 27
 
0.1%
-1 27
 
0.1%
-200 25
 
0.1%
Other values (23412) 27147
90.5%
ValueCountFrequency (%)
-1702347 1
< 0.1%
-1038728 1
< 0.1%
-1024731 1
< 0.1%
-745700 1
< 0.1%
-412824 1
< 0.1%
-391348 1
< 0.1%
-384520 1
< 0.1%
-376258 1
< 0.1%
-360785 1
< 0.1%
-357729 1
< 0.1%
ValueCountFrequency (%)
933208 1
< 0.1%
723629 1
< 0.1%
641063 1
< 0.1%
602100 1
< 0.1%
591943 1
< 0.1%
585655 1
< 0.1%
581775 1
< 0.1%
575895 1
< 0.1%
572677 1
< 0.1%
566404 1
< 0.1%

overdue pay_mon3
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23001
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41787.473
Minimum-854641
Maximum1542258
Zeros2890
Zeros (%)9.6%
Negative4265
Negative (%)14.2%
Memory size234.5 KiB
2024-06-30T11:44:44.171425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-854641
5-th percentile-5466.5
Q1262.75
median17769
Q356294.25
95-th percentile179756
Maximum1542258
Range2396899
Interquartile range (IQR)56031.5

Descriptive statistics

Standard deviation69295.361
Coefficient of variation (CV)1.6582807
Kurtosis17.366827
Mean41787.473
Median Absolute Deviation (MAD)17772
Skewness2.5328619
Sum1.2536242 × 109
Variance4.801847 × 109
MonotonicityNot monotonic
2024-06-30T11:44:44.487632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2890
 
9.6%
780 64
 
0.2%
2400 41
 
0.1%
2500 39
 
0.1%
-390 36
 
0.1%
390 30
 
0.1%
-150 27
 
0.1%
-780 27
 
0.1%
-1 24
 
0.1%
150 24
 
0.1%
Other values (22991) 26798
89.3%
ValueCountFrequency (%)
-854641 1
< 0.1%
-490073 1
< 0.1%
-386187 1
< 0.1%
-370996 1
< 0.1%
-365918 1
< 0.1%
-361020 1
< 0.1%
-358927 1
< 0.1%
-340547 1
< 0.1%
-337516 1
< 0.1%
-337200 1
< 0.1%
ValueCountFrequency (%)
1542258 1
< 0.1%
854454 1
< 0.1%
663643 1
< 0.1%
659627 1
< 0.1%
614820 1
< 0.1%
576564 1
< 0.1%
572677 1
< 0.1%
568447 1
< 0.1%
562798 1
< 0.1%
555715 1
< 0.1%

overdue pay_mon4
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22682
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38436.872
Minimum-667000
Maximum841586
Zeros3118
Zeros (%)10.4%
Negative4096
Negative (%)13.7%
Memory size234.5 KiB
2024-06-30T11:44:44.779458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-667000
5-th percentile-4440
Q1230
median16970
Q350259.5
95-th percentile166742.1
Maximum841586
Range1508586
Interquartile range (IQR)50029.5

Descriptive statistics

Standard deviation64200.611
Coefficient of variation (CV)1.6702871
Kurtosis10.872025
Mean38436.872
Median Absolute Deviation (MAD)16970
Skewness2.3643581
Sum1.1531062 × 109
Variance4.1217184 × 109
MonotonicityNot monotonic
2024-06-30T11:44:45.080554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3118
 
10.4%
780 89
 
0.3%
-150 49
 
0.2%
2400 40
 
0.1%
390 31
 
0.1%
2500 30
 
0.1%
-390 30
 
0.1%
-1 24
 
0.1%
-18 24
 
0.1%
1050 23
 
0.1%
Other values (22672) 26542
88.5%
ValueCountFrequency (%)
-667000 1
< 0.1%
-502024 1
< 0.1%
-415069 1
< 0.1%
-413122 1
< 0.1%
-330982 1
< 0.1%
-316943 1
< 0.1%
-299159 1
< 0.1%
-294999 1
< 0.1%
-281225 1
< 0.1%
-274421 1
< 0.1%
ValueCountFrequency (%)
841586 1
< 0.1%
691864 1
< 0.1%
572805 1
< 0.1%
556792 1
< 0.1%
551811 1
< 0.1%
549435 1
< 0.1%
545669 1
< 0.1%
534020 1
< 0.1%
526372 1
< 0.1%
523016 1
< 0.1%

overdue pay_mon5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22058
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35512.013
Minimum-414380
Maximum877171
Zeros3364
Zeros (%)11.2%
Negative4356
Negative (%)14.5%
Memory size234.5 KiB
2024-06-30T11:44:45.400553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-414380
5-th percentile-4531.05
Q10
median15538
Q346961.5
95-th percentile157664.75
Maximum877171
Range1291551
Interquartile range (IQR)46961.5

Descriptive statistics

Standard deviation60553.37
Coefficient of variation (CV)1.7051517
Kurtosis11.574377
Mean35512.013
Median Absolute Deviation (MAD)15538
Skewness2.4636987
Sum1.0653604 × 109
Variance3.6667106 × 109
MonotonicityNot monotonic
2024-06-30T11:44:45.735598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3364
 
11.2%
780 83
 
0.3%
-150 74
 
0.2%
150 46
 
0.2%
390 42
 
0.1%
2400 37
 
0.1%
2500 32
 
0.1%
-780 31
 
0.1%
-18 31
 
0.1%
-1 27
 
0.1%
Other values (22048) 26233
87.4%
ValueCountFrequency (%)
-414380 1
< 0.1%
-413122 1
< 0.1%
-375537 1
< 0.1%
-330982 1
< 0.1%
-312435 1
< 0.1%
-306663 1
< 0.1%
-302699 1
< 0.1%
-301088 1
< 0.1%
-296730 1
< 0.1%
-290275 1
< 0.1%
ValueCountFrequency (%)
877171 1
< 0.1%
805167 1
< 0.1%
551863 1
< 0.1%
535865 1
< 0.1%
526372 1
< 0.1%
525702 1
< 0.1%
506815 1
< 0.1%
501474 1
< 0.1%
498139 1
< 0.1%
494143 1
< 0.1%

overdue pay_mon6
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21718
Distinct (%)72.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33656.258
Minimum-684896
Maximum911408
Zeros3854
Zeros (%)12.8%
Negative4527
Negative (%)15.1%
Memory size234.5 KiB
2024-06-30T11:44:46.990816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-684896
5-th percentile-5459.1
Q10
median13926.5
Q346067.25
95-th percentile153520.35
Maximum911408
Range1596304
Interquartile range (IQR)46067.25

Descriptive statistics

Standard deviation60151.291
Coefficient of variation (CV)1.7872246
Kurtosis11.924636
Mean33656.258
Median Absolute Deviation (MAD)13926.5
Skewness2.1582519
Sum1.0096877 × 109
Variance3.6181778 × 109
MonotonicityNot monotonic
2024-06-30T11:44:47.274709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3854
 
12.8%
150 74
 
0.2%
780 73
 
0.2%
-150 52
 
0.2%
-780 39
 
0.1%
-18 33
 
0.1%
2400 32
 
0.1%
-200 30
 
0.1%
390 29
 
0.1%
2500 29
 
0.1%
Other values (21708) 25755
85.9%
ValueCountFrequency (%)
-684896 1
< 0.1%
-528466 1
< 0.1%
-513745 1
< 0.1%
-470934 1
< 0.1%
-396700 1
< 0.1%
-355318 1
< 0.1%
-329482 1
< 0.1%
-313110 1
< 0.1%
-301353 1
< 0.1%
-290091 1
< 0.1%
ValueCountFrequency (%)
911408 1
< 0.1%
679638 1
< 0.1%
548638 1
< 0.1%
527496 1
< 0.1%
509566 1
< 0.1%
499975 1
< 0.1%
494918 1
< 0.1%
484100 1
< 0.1%
483211 1
< 0.1%
481792 1
< 0.1%

Interactions

2024-06-30T11:44:29.221295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:16.568540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:21.376583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:25.705757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:30.628186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:35.319163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:40.823199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:46.306761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:50.507616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:55.151494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:00.404571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:04.677522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:09.544727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:14.807469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:18.961744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:23.299938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:29.472425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:16.999393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:21.657033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:25.983999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:31.001408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:35.593081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:41.100190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:46.573814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:50.747871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:55.412532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:00.664642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:04.930212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:09.915345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:15.088350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:19.231241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:23.692411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:29.742150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:17.402273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:21.917922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:26.264180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:31.432919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:35.854929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:41.348946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:46.838608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-06-30T11:44:28.686752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:33.290606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:21.091273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:25.428510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:30.240558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:35.044720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:40.566011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:46.041995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:50.248024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:43:54.856452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:00.111366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:04.382370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:09.145348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:14.539070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:18.692324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:22.931891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-06-30T11:44:28.950551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-06-30T11:44:47.574326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AGEEDUCATIONIDLIMIT_BALMARRIAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6SEXdefault.payment.next.monthoverdue pay_mon1overdue pay_mon2overdue pay_mon3overdue pay_mon4overdue pay_mon5overdue pay_mon6
AGE1.0000.1590.0250.1860.286-0.064-0.083-0.083-0.080-0.083-0.0760.0910.048-0.008-0.008-0.006-0.013-0.011-0.011
EDUCATION0.1591.0000.033-0.2640.1140.1320.1690.1620.1520.1370.1240.0290.0720.1100.1090.0980.0850.0810.079
ID0.0250.0331.0000.0310.021-0.025-0.005-0.009-0.004-0.016-0.0060.1790.0380.0090.003-0.0020.0310.0130.014
LIMIT_BAL0.186-0.2640.0311.0000.064-0.296-0.343-0.332-0.309-0.285-0.2640.0730.1570.002-0.0080.0020.0180.0170.011
MARRIAGE0.2860.1140.0210.0641.0000.0230.0370.0420.0450.0470.0440.0320.0330.0070.0100.0050.0110.0090.011
PAY_0-0.0640.132-0.025-0.2960.0231.0000.6270.5480.5160.4860.4640.0590.4220.3770.3840.3650.3510.3460.340
PAY_2-0.0830.169-0.005-0.3430.0370.6271.0000.7990.7130.6740.6350.0710.3400.5930.5660.5330.5110.4950.479
PAY_3-0.0830.162-0.009-0.3320.0420.5480.7991.0000.8010.7180.6710.0670.2940.5110.6060.5640.5400.5160.498
PAY_4-0.0800.152-0.004-0.3090.0450.5160.7130.8011.0000.8220.7320.0630.2780.5040.5350.6320.5930.5600.533
PAY_5-0.0830.137-0.016-0.2850.0470.4860.6740.7180.8221.0000.8210.0560.2690.4900.5190.5590.6580.6090.571
PAY_6-0.0760.124-0.006-0.2640.0440.4640.6350.6710.7320.8211.0000.0470.2490.4810.5100.5410.5790.6680.615
SEX0.0910.0290.1790.0730.0320.0590.0710.0670.0630.0560.0471.0000.039-0.040-0.042-0.033-0.026-0.017-0.019
default.payment.next.month0.0480.0720.0380.1570.0330.4220.3400.2940.2780.2690.2490.0391.0000.0110.0210.0220.0210.0240.032
overdue pay_mon1-0.0080.1100.0090.0020.0070.3770.5930.5110.5040.4900.481-0.0400.0111.0000.8140.7830.7360.7070.674
overdue pay_mon2-0.0080.1090.003-0.0080.0100.3840.5660.6060.5350.5190.510-0.0420.0210.8141.0000.7990.7670.7290.698
overdue pay_mon3-0.0060.098-0.0020.0020.0050.3650.5330.5640.6320.5590.541-0.0330.0220.7830.7991.0000.7910.7610.721
overdue pay_mon4-0.0130.0850.0310.0180.0110.3510.5110.5400.5930.6580.579-0.0260.0210.7360.7670.7911.0000.7860.757
overdue pay_mon5-0.0110.0810.0130.0170.0090.3460.4950.5160.5600.6090.668-0.0170.0240.7070.7290.7610.7861.0000.772
overdue pay_mon6-0.0110.0790.0140.0110.0110.3400.4790.4980.5330.5710.615-0.0190.0320.6740.6980.7210.7570.7721.000

Missing values

2024-06-30T11:44:33.696992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-30T11:44:34.392670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDLIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6default.payment.next.monthoverdue pay_mon1overdue pay_mon2overdue pay_mon3overdue pay_mon4overdue pay_mon5overdue pay_mon6
0120000.02212422-1-1-2-213913.02413.0689.00.00.00.0
12120000.022226-12000212682.0725.01682.02272.03455.01261.0
2390000.022234000000027721.012527.012559.013331.013948.010549.0
3450000.022137000000044990.046214.048091.027214.027890.028547.0
4550000.012157-10-100006617.0-31011.025835.011940.018457.018452.0
5650000.011237000000061900.055254.056951.018394.018619.019224.0
67500000.0112290000000312965.0372023.0407007.0522414.0469253.0460174.0
78100000.0222230-1-100-1011496.0-221.0601.0-360.0-1846.0-975.0
89140000.02312800200007956.014096.011676.011211.010793.02719.0
91020000.013235-2-2-2-2-1-100.00.00.0-13007.011885.013912.0
IDLIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6default.payment.next.monthoverdue pay_mon1overdue pay_mon2overdue pay_mon3overdue pay_mon4overdue pay_mon5overdue pay_mon6
2999029991140000.0121410000000132325.0130142.0134882.0136757.047675.044121.0
2999129992210000.01213432222212500.02500.02500.02500.02500.02500.0
299922999310000.013143000-2-2-206802.010400.00.00.00.00.0
2999329994100000.0112380-1-100001042.0-110357.098996.067626.067473.053004.0
299942999580000.012234222222165557.074208.079384.070519.082607.077158.0
2999529996220000.0131390000000180448.0172815.0203362.084957.026237.014980.0
2999629997150000.013243-1-1-1-1000-154.0-1698.0-5496.08850.05190.00.0
299972999830000.012237432-10013565.03356.0-19242.016678.018582.016257.0
299982999980000.0131411-1000-11-87545.074970.075126.050848.0-41109.047140.0
299993000050000.012146000000145851.047105.048334.035535.031428.014313.0